1. Field of the Invention
The present invention relates generally to apparatus and methods for processing physiological sensor data.
2. Discussion of the Related Art
Biomedical monitoring devices such as pulse oximeters, glucose sensors, electrocardiograms, capnometers, fetal monitors, electromyograms, electroencephalograms, and ultrasounds are sensitive to noise and artifacts. Typical sources of noise and artifacts include baseline wander, electrode-motion artifacts, physiological artifacts, high-frequency noise, and external interference. Some artifacts can resemble real processes, such as ectopic beats, and cannot be removed reliably by simple filters; however, these are removable by the techniques taught herein.
Patents related to the current invention are summarized herein.
Lung Volume
M. Sackner, et. al. “Systems and Methods for Respiratory Event Detection”, U.S. patent application no. 2008/0082018 (Apr. 3, 2008) describe a system and method of processing respiratory signals from inductive plethysmographic sensors in an ambulatory setting that filters for artifact rejection to improve calibration of sensor data and to produce output indicative of lung volume.
Pulse Oximeter
J. Scharf, et. al. “Separating Motion from Cardiac Signals Using Second Order Derivative of the Photo-Plethysmograph and Fast Fourier Transforms”, U.S. Pat. No. 7,020,507 (Mar. 28, 2006) describes the use of filtering photo-plethysmograph data in the time domain to remove motion artifacts.
M. Diab, et. al. “Plethysmograph Pulse Recognition Processor”, U.S. Pat. No. 6,463,311 (Oct. 8, 2002) describe an intelligent, rule-based processor for recognition of individual pulses in a pulse oximeter-derived photo-plethysmograph waveform operating using a first phase to detect candidate pulses and a second phase applying a plethysmograph model to the candidate pulses resulting in period and signal strength of each pulse along with pulse density.
C. Baker, et. al. “Method and Apparatus for Estimating Physiological Parameters Using Model-Based Adaptive Filtering”, U.S. Pat. No. 5,853,364 (Dec. 29, 1998) describe a method and apparatus for processing pulse oximeter data taking into account physical limitations using mathematical models to estimate physiological parameters.
Cardiac
J. McNames, et. al. “Method, System, and Apparatus for Cardiovascular Signal Analysis, Modeling, and Monitoring”, U.S. patent application publication no. 2009/0069647 (Mar. 12, 2009) describe a method and apparatus to monitor arterial blood pressure, pulse oximetry, and intracranial pressure to yield heart rate, respiratory rate, and pulse pressure variation using a statistical state-space model of cardiovascular signals and a generalized Kalman filter to simultaneously estimate and track the cardiovascular parameters of interest.
M. Sackner, et. al. “Method and System for Extracting Cardiac Parameters from Plethysmograph Signals”, U.S. patent application publication no. 2008/0027341 (Jan. 31, 2008) describe a method and system for extracting cardiac parameters from ambulatory plethysmographic signal to determine ventricular wall motion.
Hemorrhage
P. Cox, et. al. “Methods and Systems for Non-Invasive Internal Hemorrhage Detection”, International Publication no. WO 2008/055173 A2 (May 8, 2008) describe a method and system for detecting internal hemorrhaging using a probabilistic network operating on data from an electrocardiogram, a photoplethysmogram, and oxygen, respiratory, skin temperature, and blood pressure measurements to determine if the person has internal hemorrhaging.
Disease Detection
V. Karlov, et. al. “Diagnosing Inapparent Diseases From Common Clinical Tests Using Bayesian Analysis”, U.S. patent application publication no. 2009/0024332 (Jan. 22, 2009) describe a system and method of diagnosing or screening for diseases using a Bayesian probability estimation technique on a database of clinical data.
Statement of the Problem
The influence of multiple sources of contaminating signals often overlaps the frequency of the signal of interest, making it difficult, if not impossible, to apply conventional filtering. Severe artifacts such as occasional signal dropouts due to sensor movement or large periodic artifacts are also difficult to filter in real time. Biological sensor hardware can be equipped with a computer comprising software for post-processing data and reducing or rejecting noise and artifacts. Current filtering techniques typically use some knowledge of the expected frequencies of interest where the sought-after physiological information should be found, and do not contain a mathematical model describing either the physiological processes that are measured or the physical processes that measure the signal.
Adaptive filtering has been used to attenuate artifacts in pulse oximeter signals corrupted with overlapping frequency noise bands by estimating the magnitude of noise caused by patient motion and other artifacts, and canceling its contribution from pulse oximeter signals during patient movement. Such a time correlation method relies on a series of assumptions and approximations to the expected signal, noise, and artifact spectra, which compromises accuracy, reliability and general applicability.
Biomedical filtering techniques based on Kalman and extended Kalman techniques offer advantages over conventional methods and work well for filtering linear systems or systems with small nonlinearities and Gaussian noise. These filters, however, are not adequate for filtering highly nonlinear systems and non-Gaussian/non-stationary noise. Therefore, obtaining reliable biomedical signals continue to present problems, particularly when measurements are made in mobile, ambulatory, and physically active patients.
Existing data processing techniques, including adaptive noise cancellation filters, are unable to extract information that is hidden or embedded in biomedical signals and also discard some potentially valuable information.